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Build your first AI data analyst chatbot

SolomonSolomon
162142 views
2/3/2026
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Enhance your data analysis by connecting an AI Agent to your dataset, using n8n tools.

This template teaches you how to build an AI Data Analyst Chatbot that is capable of pulling data from your sources, using tools like Google Sheets or databases. It's designed to be easy and efficient, making it a good starting point for AI-driven data analysis.

You can easily replace the current Google Sheets tools for databases like Postgres or MySQL.

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How It Works

The core of the workflow is the AI Agent. It's connected to different data retrieval tools, to get data from Google Sheets (or your preferred database) in many different ways.

Once the data is retrieved, the Calculator tool allows the AI to perform mathematical operations, making your data analysis precise.

Who is this template for

  • Data Analysts & Researchers: Pull data from different sources and perform quick calculations.
  • Developers & AI Enthusiasts: Learn to build your first AI Agent with easy dataset access.
  • Business Owners: Streamline your data analysis with AI insights and automate repetitive tasks.
  • Automation Experts: Enhance your automation skills by integrating AI with your existing databases.

How to Set Up

You can find detailed instructions in the workflow itself.

Check out my other templates

👉 https://n8n.io/creators/solomon/

n8n AI Data Analyst Chatbot Workflow

This n8n workflow demonstrates how to build a basic AI data analyst chatbot. It leverages LangChain nodes to create a conversational agent capable of using a calculator and calling other n8n workflows as tools to answer user queries.

What it does

This workflow simplifies the creation of an AI-powered chatbot that can respond to user messages by:

  1. Listening for Chat Messages: It triggers when a new chat message is received.
  2. Initializing AI Agent: Sets up an AI agent with a conversational chat model (OpenAI) and a simple memory to maintain context.
  3. Providing Tools: Equips the AI agent with two key tools:
    • Calculator: Allows the AI to perform mathematical calculations.
    • Call n8n Workflow Tool: Enables the AI to execute other n8n workflows, extending its capabilities to interact with various services and data sources.
  4. Processing User Input: The AI agent processes the incoming chat message, determining whether to use its tools or directly respond based on the query.
  5. Aggregating Responses: Collects the AI agent's response.
  6. Filtering Responses: Checks if the AI agent provided a response.
  7. Sending HTTP Request: If a response is available, it sends an HTTP request (presumably to send the response back to the chat platform).

Prerequisites/Requirements

To use this workflow, you will need:

  • n8n Instance: A running n8n instance (cloud or self-hosted).
  • OpenAI API Key: For the OpenAI Chat Model node to function. This should be configured as an n8n credential.
  • LangChain Nodes: Ensure you have the LangChain nodes installed in your n8n instance.
  • Additional n8n Workflows (Optional): If you intend for the Call n8n Workflow Tool to be useful, you will need other n8n workflows configured to be called as tools.

Setup/Usage

  1. Import the Workflow: Download the provided JSON and import it into your n8n instance.
  2. Configure Credentials:
    • Set up an OpenAI API Key credential in n8n.
    • Ensure any credentials required by the Call n8n Workflow Tool (for the workflows it might call) are also configured.
  3. Configure OpenAI Chat Model: In the OpenAI Chat Model node, select your OpenAI API Key credential.
  4. Configure Call n8n Workflow Tool:
    • In the Call n8n Workflow Tool node, specify the IDs of the n8n workflows you want the AI to be able to call. These workflows should be designed to take input and return output in a way the AI can understand.
  5. Activate the Workflow: Enable the workflow in your n8n instance.
  6. Send Chat Messages: The workflow will now listen for incoming chat messages via the When chat message received trigger. You can test it by sending messages to the configured chat platform.

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